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Epsilon annealing #56

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Jul 26, 2017
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2 changes: 2 additions & 0 deletions tensorforce/core/explorations/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -17,13 +17,15 @@
from tensorforce.core.explorations.exploration import Exploration
from tensorforce.core.explorations.constant import Constant
from tensorforce.core.explorations.linear_decay import LinearDecay
from tensorforce.core.explorations.epsilon_anneal import EpsilonAnneal
from tensorforce.core.explorations.epsilon_decay import EpsilonDecay
from tensorforce.core.explorations.ornstein_uhlenbeck_process import OrnsteinUhlenbeckProcess


explorations = dict(
constant=Constant,
linear_decay=LinearDecay,
epsilon_anneal=EpsilonAnneal,
epsilon_decay=EpsilonDecay,
ornstein_uhlenbeck=OrnsteinUhlenbeckProcess
)
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37 changes: 37 additions & 0 deletions tensorforce/core/explorations/epsilon_anneal.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,37 @@
# Copyright 2017 reinforce.io. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

from tensorforce.core.explorations import Exploration


class EpsilonAnneal(Exploration):
"""
Annealing epsilon parameter based on ratio of current timestep to total timesteps.
"""

def __init__(self, epsilon=1.0, epsilon_final=0.1, epsilon_timesteps=10000):
self.epsilon = epsilon
self.epsilon_final = epsilon_final
self.epsilon_timesteps = epsilon_timesteps

def __call__(self, episode=0, timestep=0):
# TODO: Trim by length of `first_update`, removing steps with no learning.
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This can probably be ignored, though with large values of first_update it may have an impact.

offset = 0 # self.first_update
self.epsilon = min(1.0, max(
self.epsilon_final,
1.0-(timestep - offset)/(self.epsilon_timesteps - offset)
));

return self.epsilon
4 changes: 2 additions & 2 deletions tensorforce/core/explorations/epsilon_decay.py
Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,8 @@

class EpsilonDecay(Exploration):
"""
Linearly decaying epsilon parameter based on number of states,
an initial random epsilon and a final random epsilon.
Exponentially decaying epsilon parameter based on ratio of
difference between current and final epsilon to total timesteps.
"""

def __init__(self, epsilon=1.0, epsilon_final=0.1, epsilon_timesteps=10000):
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